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import requests
import torch
from PIL import Image
from torchvision import transforms
import gradio as gr
model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def predict(inp):
inp = Image.fromarray(inp.astype("uint8"), "RGB")
inp = transforms.ToTensor()(inp).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
return {labels[i]: float(prediction[i]) for i in range(1000)}
inputs = gr.Image()
outputs = gr.Label(num_top_classes=3)
demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)
if __name__ == "__main__":
demo.launch()
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